Many systems experience loads having quasi-periodic properties. For example, equipment containing rotating load-bearing parts (e.g., rotorcraft, jet engines, HVAC chillers, electric motors, etc.) is used in many applications.
To determine when a given part needs to be repaired or replaced, there needs to be a way of calculating the actual rate of wear or damage accumulation for parts that degrade as a function of system usage and loads as opposed to time-based or random damage accumulation. Often it is not practical to physically measure operational loads. In such cases currently known methods use statistical analysis of loads measured in qualification tests of prototype equipment. However, because the actual operational loads experienced by a given part are unknown, the statistical analysis assumes a statistically worst-case damage accumulation for each component to calculate a conservative safe useful life, retirement time or time between overhaul and repair (TBO). These analyses often define calculated retirement times (CRTs) or TBOs in terms of hours of usage assuming a constant rate of wear and damage accumulation per hour of operation. This allows practical time-based maintenance schedules without the need to add numerous physical sensors to the product, which would result in a heavier, costlier, more complex product. However, it results in products either being over designed and/or products that experience average usage and loads being repaired or retired long before they would need to be if CRTs and TBOs could be calculated on an on-going basis using actual operational usage and loads.
If the actual equipment operation is similar to the worst-case assumptions used during design of the components, actual CRTs or TBOs should be close to the a priori conservative CRTs or TBOs based on test data. As a practical matter, however, most equipment will be used in conditions that are much less severe than the worst-case assumptions. For example, a rotorcraft not used in combat will contain parts that will accumulate wear and damage at a slower rate than maintenance schedules assume. Thus, a time-based maintenance schedule would therefore require repair or component replacement earlier than a usage-based maintenance (UBM) system. For example, if a part is designed with CRT of 10,000 hours, a part that has been used for 10,000 hours in mild conditions would probably not require replacement until much later. This causes average equipment to be down for component maintenance even when the component has a significant remaining service life. This unneeded downtime increases equipment operating costs and causes gaps in equipment availability.
Component wear and damage accumulation is highly dependent on the conditions in which the component is used. Because of these varying operational conditions, it would be desirable to monitor the actual loads on a part during equipment use in order to enable usage-based maintenance that would maximize part CRTs and/or TBOs and minimize maintenance costs. This capability would also enable optimized part design and weight management, optimized logistical supply chains, and as well as other applications. This would allow the component CRT to be extended and/or the component to be redesigned to reduce weight while maintaining the same maintenance life for the average equipment. However, the location and operation of the parts may make mounting and monitoring load sensors difficult or cumbersome, particularly for rotating parts, which would require data transmission between a moving sensor and a fixed receiver. Adding load sensors to all of the parts to be monitored increases the complexity of the equipment and requires additional electronics, which increase system weight and cost. It also has a detrimental effect on the complexity and cost of certification of additional equipment for load monitoring.
Virtual sensors were proposed to allow monitoring of actual system operational loads without adding physical sensors to the system and thus enable cost-effective usage-based maintenance (UBM) processes. A virtual sensor is a transfer function that provides a statistically accurate estimate of a desired system measurement (e.g. a structural load) using readily available system state parameters (e.g. speed, weight, load factors, control settings, etc.) as inputs. The accuracy of the virtual sensor depends upon mathematical functions utilized to construct the mapping between the desired measurement and the state parameters. It also depends upon the informally defined quality of the data set utilized to optimize parameters of the transfer function. Such data contain pairs of desired measurements and corresponding state parameters, where pairs could be derived from various sorts of data (e.g. obtained from first principles, simulations, bench or flight tests). When the transfer function is created it is usually tested using blind test to exhibit robust correlations between desired measurements and state parameters.
There are several factors that impact the accuracy of the transfer function. First, even systems that are nominally identical when new (e.g. multiple aircraft of the same model made at different times) may have unintended variations in physical characteristics due to manufacturing variations and changes in the manufacturing process and components over time. Thus, the actual or simulated system which provided data for developing the transfer function may have some differences from the target systems on which the virtual sensors will be eventually implemented and utilized over a significant portion of the system life span.
Additionally, the systems may change over time due to age induced wear and/or modifications (such as hardware additions or modifications). These changes to specific systems will make the predetermined mapping less accurate and increase the variation between systems, which are otherwise nominally identical.
Another possible deficiency of virtual sensors is detecting and handling conditions that are outside of the domains of state parameters that have been mapped and lack of ability to model non-deterministic or random loads due to ballistic impact, severe overload (e.g., hard landing) or other off-design conditions.